The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
A null‐model for significance testing of presence‐only species distribution models
Abstract
Species' distribution models (SDMs) attempt to predict the potential distribution of species by interpolating identified relationships between species' presence/ absence, or presence-only data on one hand, and environmental predictors on the other hand, to a geographical area of interest. Currently, they are widely applied in biogeography, conservation biology, ecology, palaeo-ecology, invasive species studies, and wildlife management (Guisan and Zimmermann 2000, Araijo and Pearson 2005, Thuiller et al. 2005, Peterson 2006, Aratijo and Guisan 2006, Guisan et al. 2006). More recently, vast numbers of herbarium and natural history museum collections have become available (Graham et al. 2004) and techniques to apply this special type of presence-only data have been developed (Hirzel et al. 2002, Anderson et al. 2003, Pearce and Boyce 2006, Elith et al. 2006, Phillips et al. 2006). Despite the widespread use of SDMs, several high-priority research interests remain to be investigated (Guisan and Thuiller 2005, Aradjo and Guisan 2006). One of these is the improvement of SDM validation, or the quantification of a model's predictive performance (Araijo and Guisan 2006). The fact that the standard validation procedures for an SDM are not sufficient to assess the applicability of an SDM in a predictive context, was first shown by Olden et al. (2002). They showed that after SDM validation it is critical to assess whether the
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Cites
Very high resolution interpolated climate surfaces for global land areas
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Maximum entropy modeling of species geographic distributions
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Measuring the Accuracy of Diagnostic Systems
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Novel methods improve prediction of species’ distributions from occurrence data
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Predictive habitat distribution models in ecology
Guisan, A., & Zimmermann, N. E. (2000) · Ecological Modelling · doi:10.1016/s0304-3800(00)00354-9 · Cited by 7,359
A review of methods for the assessment of prediction errors in conservation presence/absence models
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Selecting thresholds of occurrence in the prediction of species distributions
Liu, C., Berry, P. M., Dawson, T. P., & Pearson, R. G. (2005) · Ecography · doi:10.1111/j.0906-7590.2005.03957.x · Cited by 2,701
Cited by
Selecting thresholds for the prediction of species occurrence with presence‐only data
Liu, C., White, M., & Newell, G. (2013) · Journal of Biogeography · doi:10.1111/jbi.12058 · Cited by 1,613
The importance of correcting for sampling bias in MaxEnt species distribution models
Kramer‐Schadt, S., Niedballa, J., Pilgrim, J. D., Schröder, B., Lindenborn, J., Reinfelder, V., Stillfried, M., Heckmann, I., Scharf, A. K., Augeri, D. M., et al. (2013) · Diversity and Distributions · doi:10.1111/ddi.12096 · Cited by 1,321
Valavi, R., Guillera‐Arroita, G., Lahoz‐Monfort, J. J., & Elith, J. (2022) · Ecological Monographs · doi:10.1002/ecm.1486 · Cited by 830
What do we gain from simplicity versus complexity in species distribution models?
Merow, C., Smith, M. J., Edwards, T. C., Guisan, A., McMahon, S. M., Normand, S., Thuiller, W., Wüest, R. O., Zimmermann, N. E., & Elith, J. (2014) · Ecography · doi:10.1111/ecog.00845 · Cited by 683
The uncertain nature of absences and their importance in species distribution modelling
Lobo, J. M., Jiménez‐Valverde, A., & Hortal, J. (2010) · Ecography · doi:10.1111/j.1600-0587.2009.06039.x · Cited by 656
Metadata
- Authors
- Raes, N., & ter Steege, H.
- Venue
- Ecography
- Publisher
- Wiley
- Published
- 2007-09-27
- Volume
- 30
- Issue
- 5
- Pages
- 727-736
- ISSN
- 0906-7590 (ISSN-L)
- 1600-0587
- OpenAlex
- W2027575815
- Cited by
- 537
- References
- 42
- FWCI
- 7.06
- Type
- journal article
- Language
- en
- Open access
- closed
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